Ethan Franco

About

Education: Stony Brook University, B.S. Physics, 2024

Research Interests: Computational Astrophysics and Cosmology, Machine Learning, Optimization, Aerospace Engineering, and Space Exploration.
Check out my research!

Publications:
The database of spectroscopic constants of diatomic molecules (DSCDM): A dynamic and user-friendly interface for molecular physics and spectroscopy

Presentations:
Presented work on DSCDM (DMSD at the time) during Undergradute Research Day at Stony Brook University

A Little About Me:
As you may have already noticed, I really enjoy spending time outdoors. Specifically, mountaineering and backpacking. Spending time in the mountains is a great way to clear my head and get away from the hustle and bustle of everyday life. But it's also challenging, and I enjoy pushing myself to my limits. I have a tendency to choose the most difficult routes I can find (to my friends' dismay).

I also enjoy photography, and so I tend to bring my camera along with me on my adventures. All photos on this website were taken by me (other than the JWST photo in the research section). If you're interested in seeing more of my photos, you can check out my photos section.

Research

Stony Brook University - Strong Lensing
Developing advanced lens models using the 'lenstronomy' Python library to predict time delays in the triply-imaged type Ia 'Supernova H0pe' within galaxy clusters. This work is crucial for understanding cosmic scales and refining the Hubble constant (H0) by analyzing how galaxy clusters magnify and distort light from distant supernovae. Our goal is to enhance the accuracy of these models and deepen our knowledge of gravitational lensing phenomena, aiding in the exploration of dark matter distribution in the universe and contributing to key debates in cosmology

Stony Brook University - The Database of Spectroscopic Constants of Diatomic Molecules (DSCDM)
In collaboration with a team, I contributed to the development of the Database for Spectroscopic Constants of Diatomic Molecules (DSCDM), integrating machine learning with to predict properties of untested molecules. This effort, coupled with advanced data analysis techniques, significantly improved the database's accuracy and usability. Our work has facilitated more efficient and precise research in molecular spectroscopy.

Experience

Brookhaven National Laboratory - Research Assistant (Current)
Currently working on optimizing the Central Reconstruction Service (CRS) in the Scientific Data and Computing Center.

Stony Brook University - Software Developer (Current)
Building automation tools for the Integrative Neuroscience Group at Stony Brook University.

Polen Capital - Data Engineer Intern (2023)
I spearheaded the integration of Python-Azure data pipelines, automating data reporting processes and yielding significant time savings of 12.65 hours daily, while also cutting down on third-party subscription costs. This initiative notably reduced the time required for monthly and quarterly tasks by 100 and 15 hours respectively. Additionally, I developed a scalable Python API using Azure Functions, which facilitated secure and expandable cloud-based web scraping, enhancing our data acquisition capabilities.

Formlabs - R&D Engineer Intern (2022)
I designed and implemented full-stack automation tools using Python and JavaScript (React.js), significantly streamlining materials research and development processes. In a key project, I developed a force model and executed a design of experiments that achieved a 40% reduction in support structure generation time, while maintaining high print stability and success rates. Additionally, I optimized industrial photopolymer resins, cutting print times by an average of 3 hours and achieving a 100% success rate during validation. My work also included expanding optimization-related tooling for the Selective Laser Sintering (SLS) material development team, which enhanced print processes and quality

Wolfspeed - Process Engineer Intern (2021)
I conducted an analysis of historical Physical Vapor Deposition (PVD) data using Microsoft Access and SAS, pinpointing the root causes of AU spitting events during the deposition process. Building on this analysis, I developed a real-time, data-driven predictive model to monitor deposition, which successfully reduced out-of-specification batches by 30% and facilitated optimized post-processing. The implementation of this predictive model across the company significantly enhanced performance efficiency and improved overall product quality.

Photos

Hello! This is WIP

Contact

Email: ethanjfranco@gmail.com

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Elements

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    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';